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We aimed to assess ChatGPT's performance on the Clinical Informatics Board Examination and to discuss the implications of large language models (LLMs) for board certification and maintenance. We tested ChatGPT using 260 multiple-choice questions from Mankowitz's Clinical Informatics Board Review book, omitting 6 image-dependent questions. ChatGPT answered 190 (74%) of 254 eligible questions correctly. While performance varied across the Clinical Informatics Core Content Areas, differences were not statistically significant. ChatGPT's [...]

To train and test a model predicting chronic kidney disease (CKD) using the Generalized Additive2 Model (GA2M), and compare it with other models being obtained with traditional or machine learning approaches.

To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications.

Successful model development requires both an accurate a priori understanding of future performance and high performance on deployment. Optimistic estimations of model performance that are unrealized in real-world clinical settings can contribute to nonuse of predictive models. This study used 2 tasks, predicting ICU mortality and Bi-Level Positive Airway Pressure failure, to quantify: (1) how well internal test performances derived from different methods of partitioning data into development and test [...]

Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a "sick care" system into a "health-promoting" system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and [...]

Artificial intelligence (AI) has the potential to be a disruptive technology in healthcare. Recently, there is increased speculation that AI may be used to replace healthcare providers in the future. To answer this question, we reviewed over 21 000 articles published in medical specialty journals between 2019 and 2021 to evaluate whether these AI models were intended to assist or replace healthcare providers. We also evaluated whether all Food and Drug [...]

Measures of diagnostic performance in cancer are underdeveloped. Electronic clinical quality measures (eCQMs) to assess quality of cancer diagnosis could help quantify and improve diagnostic performance.

To derive a comprehensive implementation framework for clinical AI models within hospitals informed by existing AI frameworks and integrated with reporting standards for clinical AI research.

 Effective communication between surgeons and anesthesiologists is critical for high-quality, safe, and efficient perioperative patient care. Despite widespread implementation of surgical safety checklists and time-outs, ineffective team communication remains a leading cause of patient safety events in the operating room. To promote effective communication, we conducted a pilot trial of a "virtual huddle" between anesthesiologists and surgeons.

Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care.